code: def test_multiclass():
      X, y = sklearn.datasets.make_classification(
          n_classes=3, n_informative=4
      )
      X = da.from_array(X, chunks=50)
      y = da.from_array(y, chunks=50)
  
      clf = ParallelPostFit(
          LogisticRegression(
              random_state=0,
              n_jobs=1,
              solver="lbfgs",
              multi_class="auto",
          )
      )
  
      clf.fit(*dask.compute(X, y))
      result = clf.predict(X)
      expected = clf.estimator.predict(X)
  
      assert isinstance(result, da.Array)
      assert_eq_ar(result, expected)
  
      result = clf.predict_proba(X)
      expected = clf.estimator.predict_proba(X)
  
      assert isinstance(result, da.Array)
      assert_eq_ar(result, expected)
  
      result = clf.predict_log_proba(X)
      expected = clf.estimator.predict_log_proba(X)
      assert_eq_ar(result, expected)
  
imports: import dask
  import dask.array
  import dask.dataframe
  import numpy
  import pandas
  import pytest
  import sklearn.datasets
  from scipy.sparse import csr_matrix
  from sklearn.decomposition import PCA
  from sklearn.ensemble import GradientBoostingClassifier
  from sklearn.linear_model import LinearRegression, LogisticRegression, SGDClassifier
  from sklearn.naive_bayes import CategoricalNB
  from sklearn.preprocessing import OneHotEncoder
  from dask_ml.datasets import make_classification
  from dask_ml.utils import assert_eq_ar, assert_estimator_equal
  from dask_ml.wrappers import ParallelPostFit
label_desc: Use image node_id 10 for calling a global function with example usage: test_multiclass() without return types